首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Modelling and prediction of retention in high-performance liquid chromatography by using neural networks
Authors:Y L Xie  J J Baeza-Baeza  J R Torres-Lapsió  M C García-Alvarez-Coque  G Ramis-Ramos
Institution:(1) Department of Environmental Chemistry, CID-CSIC, c/Jordi Girona, 18-26. 03034 Barcelona, Spain
Abstract:Summary Two analytical methods have been developed for the determination in water of 18 priority phenolics listed in US EPA method 604 and on EEC list 76/464. A solidphase extraction system using eight different sorbents packed in a precolumn was coupled on-line with a liquid chromatograph with UV detection. The ensuing method uses 50–100 mL of ground water; its performance was compared with that of an off-line method using Empore extraction disks and 1 L water samples. Phenol recoveries varied from <20 to 100% for concentrations in the range 0.1–10 mgrg/L at an acid pH. The presence of the phenols in water was confirmed by using thermospray LC-mass spectrometry in the negative ion mode. The stability of the phenols in water was studied at a 10 mgrg/l level in ground and estuarine water at acid pH (2.5–3) and at 4°C for 1 month. The system was validated by various interlaboratory exercises with samples containing 2,4,6-trichlorophenol and pentachlorophenol at concentrations from 0.1 to 0.5 mgrg/L.
Keywords:Solid-phase extraction  Liquid chromatography  Phenols  Water
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号